data = [ ‘ACME‘, 50, 91.1, (2012, 12, 21) ] name, _, price, date = data
s = [1,2,3,4,5] a, *b, c = s >>> a为1; b为[2,3,4]; c为5.
record = (‘ACME‘, 50, 123.45, (12, 18, 2012)) name, *_, (*_, year) = record
使用deque实现长度为n的FIFO队列。 deque(maxlen=N) 会创建一个大小为N的FIFO队列。
deque有如下函数: append、appendleft、pop、popleft。
from collections import deque def search(lines, pattern, history=5): previous_lines = deque(maxlen=history) for line in lines: if pattern in line: yield line, previous_lines previous_lines.append(line)
这里使用生成器函数可使搜索过程代码和使用搜索结果代码解耦。
通常采用min、max函数求得最大/小值。
通常采用nlargest、nsmallest函数
portfolio = [ {‘name‘: ‘IBM‘, ‘shares‘: 100, ‘price‘: 91.1}, {‘name‘: ‘AAPL‘, ‘shares‘: 50, ‘price‘: 543.22}, {‘name‘: ‘FB‘, ‘shares‘: 200, ‘price‘: 21.09}, {‘name‘: ‘HPQ‘, ‘shares‘: 35, ‘price‘: 31.75}, {‘name‘: ‘YHOO‘, ‘shares‘: 45, ‘price‘: 16.35}, {‘name‘: ‘ACME‘, ‘shares‘: 75, ‘price‘: 115.65} ] cheap = heapq.nsmallest(3, portfolio, key=lambda s: s[‘price‘]) expensive = heapq.nlargest(3, portfolio, key=lambda s: s[‘price‘])
nums = [1, 8, 2, 23, 7, -4, 18, 23, 42, 37, 2] import heapq heap = list(nums) heapq.heapify(heap)
heap >>> [-4, 2, 1, 23, 7, 2, 18, 23, 42, 37, 8]
# 弹出堆顶元素
heapq.heappop(heap)
import heapq class PriorityQueue: def __init__(self): self._queue = [] self._index = 0 def push(self, item, priority): heapq.heappush(self._queue, (-priority, self._index, item)) self._index += 1 def pop(self): return heapq.heappop(self._queue)[-1]
-priority 使元素优先级从高到低排列， index 保障了优先级相同的元素按照插入先后顺序排列。
原文：https://www.cnblogs.com/tomoka/p/10809355.html